Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, Assam 786004, India.
Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, Assam 786004, India.
Comput Biol Chem. 2024 Jun;110:108048. doi: 10.1016/j.compbiolchem.2024.108048. Epub 2024 Mar 2.
The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein's crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.
疟原虫对药物的抗药性不断上升,使得目前的治疗方法失效,这阻碍了消除疟疾的努力。为了解决这个问题,该研究采用了系统生物学方法和基于结构的药效基团方法的组合,以确定针对疟原虫 falciparum 的靶标。通过文本挖掘,提取了 448 个基因,发现疟原虫属中的 plasmepsins 在寄生虫的生存中起着至关重要的作用。使用 PlasmoDB 基因组数据库确定了这些蛋白质的代谢途径,并使用 CellDesigner 4.4.2 重新创建。为了确定一个有效的靶标,选择了 Plasmepsin V(PF13_0133),并使用 STRING 数据库检查其蛋白质-蛋白质相互作用(PPIs)。拓扑分析和基于全局的方法确定 PF13_0133 具有最高的中心性。此外,静态蛋白质敲除 PPIs 表明 PF13_0133 在建模网络中的重要性。由于缺乏蛋白质的晶体结构,对其进行了建模并进行了分子动力学模拟研究。基于结构的药效基团建模利用建模的 PF13_0133(PfPMV),生成了 10 个药效基团假说,其中包含针对 PfPMV 的活性和非活性化合物库。通过虚拟筛选,确定了两种潜在的候选药物,橙皮苷和芦丁,它们可能被重新用作潜在的抗疟药物。